Preprint | Xi Zhang

CCS: Clinical Consensus Selection for Radiology Report Generation

Fri, 29 May 2026 00:00:00 +0000

For the latest updates and details, visit the CCS Project Website.

Why CCS? 🔬

Most radiology MLLMs commit to a single decoded report token by token, so one unfavourable step can omit a finding or assert one unsupported by the image, with no way to recover. Yet a fixed model often places clinically stronger reports elsewhere in its candidate pool — the bottleneck is not what the model can generate, but which candidate it commits to.

How CCS Works ⚙️

Clinical Consensus Selection (CCS) is a reference-free, decoder-agnostic inference-time framework that reframes RRG as candidate selection — no retraining or extra parameters.

Rollout sampling: draw N candidate reports from the radiology MLLM via stochastic decoding.
Textual consensus utility: repurpose report-evaluation metrics as reference-free pairwise agreement scores.
Image-grounded utility: measure candidate agreement with Qwen3-VL-Embed, a multimodal embedder adapted on image–report pairs, capturing clinical agreement beyond surface text.
Highest-consensus selection: return the candidate with the highest mean pairwise consensus over the pool.

Key Contributions ✨

Inference-time perspective: We show candidate pools routinely contain reports with higher clinical reliability and consistency than single-path outputs.
CCS framework: A decoder-agnostic Best-of-N method that aggregates pairwise clinical consensus using both textual and image–report-adapted multimodal utilities.
Consistent gains: Across three datasets and multiple radiology MLLMs, CCS improves RRG at inference time and identifies image-grounded utility as a distinct selection axis.

Main Results 📈

On MIMIC-CXR, CCS improves all radiology-specific metrics over Sampling (e.g. RadGraph-F1 0.1989 → 0.2134, CheXbert-F1⁵ 0.5041 → 0.5370), with gains statistically significant (p < 0.05). Unlike generic Best-of-N selectors (Self-Certainty, ModeX) that give inconsistent or negative clinical gains, CCS improves consistently across all backbones and datasets.

Analysis 📊

Per-label F1 shows CCS recovers abnormal findings that text-only consensus suppresses — confirming that image-grounded utility is a selection axis distinct from textual consensus, with substantial headroom still remaining.

Case Study 🔍

On a real MIMIC-CXR case, single-path decoding asserts factual errors (false “clear lungs”, mislocalised catheter), whereas CCS selects a more image-grounded report (CheXbert-F1⁵ 1.0000 vs Sampling 0.5000).

BibTeX 📚

@article{zhang2026ccsclinicalconsensusselection,
 title={CCS: Clinical Consensus Selection for Radiology Report Generation},
 author={Xi Zhang and Yingshu Li and Zaiqiao Meng and Jake Lever and Edmond S. L. Ho},
 year={2026},
 eprint={2605.30131},
 archivePrefix={arXiv},
 primaryClass={cs.CL},
 url={https://arxiv.org/abs/2605.30131},
}

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

Mon, 09 Mar 2026 00:00:00 +0000

Part of the EvoScientist project — harnessing vibe research with self-evolving AI scientists.

Extend it with EvoSkills — installable skill & knowledge packs that add domain-specific expertise to AI scientists.

Overview

EvoScientist is an evolving multi-agent AI scientist framework that continuously improves its research strategies through persistent memory and self-evolution. It addresses a key limitation of existing AI-scientist systems: static, hand-designed pipelines that overlook promising directions, repeat failed experiments, and pursue infeasible ideas.

Framework

EvoScientist coordinates three specialized agents:

Researcher Agent (RA): scientific idea generation.
Engineer Agent (EA): experiment implementation and execution.
Evolution Manager Agent (EMA): distills insights from prior interactions into reusable knowledge.

These are backed by two persistent memory modules — an ideation memory (feasible directions distilled from top-ranked ideas, plus previously unsuccessful ones) and an experimentation memory (effective data-processing and training strategies from code-search trajectories and best-performing implementations) — which the RA and EA retrieve to improve idea quality and code execution success over time.

Key Results

Outperforms 7 open-source and commercial state-of-the-art systems in scientific idea generation, with higher novelty, feasibility, relevance, and clarity under both automatic and human evaluation.
Substantially improves code execution success rates through multi-agent evolution, demonstrating the effectiveness of persistent memory for end-to-end scientific discovery.

BibTeX

@article{lyu2026evoscientist,
 title={EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery},
 author={Lyu, Yougang and Zhang, Xi and Yi, Xinhao and Zhao, Yuyue and Guo, Shuyu and Hu, Wenxiang and Piotrowski, Jan and Kaliski, Jakub and Urbani, Jacopo and Meng, Zaiqiao and others},
 journal={arXiv preprint arXiv:2603.08127},
 year={2026}
}

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Sun, 10 Aug 2025 00:00:00 +0000

For the latest updates and details, visit the Awesome Self-Evolving Agents.

Background and Development Trends

This survey provides a comprehensive overview of the latest advances in the field of self-evolving AI agents, highlighting key technological shifts and development trends.

Paradigm Definition

We define the new paradigm of “Self-Evolving AI Agents” as bridging foundation models and lifelong agentic systems.

Concept Definition of Self-Evolving AI Agents

Self-evolving AI agents are autonomous systems that continuously and systematically optimise their internal components through interaction with environments, with the goal of adapting to changing tasks, contexts and resources while preserving safety and enhancing performance.

Guiding Principles: Three Laws

Inspired by Asimov’s Three Laws of Robotics, we propose the Three Laws of Self-Evolving AI Agents:

Endure (Safety Adaptation): Any modification must maintain safety and stability.
Excel (Performance Preservation): Subject to the first law, agents must preserve or enhance task performance.
Evolve (Autonomous Evolution): Subject to the first and second laws, agents must autonomously optimize their internal components in response to changes.

Unified Framework and Technical Review

We propose a unified conceptual framework with four core components: System Inputs, Agent System, Environment, and Optimisers. The survey systematically reviews evolution strategies across foundation models, prompts, memory, tools, workflows, and inter-agent communication.

Single-Agent Optimisation

We explore optimisation techniques for individual self-evolving agents, focusing on methods that enhance their learning and adaptation capabilities.

Multi-Agent Optimisation

We investigate strategies for optimising the performance of multiple self-evolving agents working collaboratively, addressing challenges such as communication, coordination, and resource sharing.

Domain Adaptation and Challenges

We also analyze domain-specific evolution methods in biomedicine, programming, and finance, and discuss key challenges in evaluation, safety, and ethics, laying the foundation for the next generation of adaptive, autonomous, lifelong agentic systems.

BibTeX

@article{fang2025comprehensive,
 title={A comprehensive survey of self-evolving ai agents: A new paradigm bridging foundation models and lifelong agentic systems},
 author={Fang, Jinyuan and Peng, Yanwen and Zhang, Xi and Wang, Yingxu and Yi, Xinhao and Zhang, Guibin and Xu, Yi and Wu, Bin and Liu, Siwei and Li, Zihao and others},
 journal={arXiv preprint arXiv:2508.07407},
 year={2025}
}